1. Field of the Invention
The present invention relates to an electronic system for data management. In particular, it relates to a database management system that dynamically characterizes and classifies data, with or without administrative assistance, i.e., soft classing. The present invention further relates to a method for automatic, human unassisted, database creation and administration.
2. Description of Related Art
Since the advent of the first computer systems in the late 1940's, management of data has shifted more and more from manually kept books and charts to vast electronic databases. For example, department store inventories were once managed by a complex manual system of accounting books. In a men's shirt department, information such as shirt style, color, neck size, sleeve length, etc. had to be manually tracked to ensure sufficient quantities of shirts types and to account for sales of each shirt. Similarly, production lines were once manually managed and tracked. Complex manual paper systems were set up by manufacturers to track quantities of incoming raw materials, the amounts of such materials used in the manufacture of batches of items, and the shipment of the completed items.
Electronic data management systems have largely replaced the complex manual systems of yesterday. Typically, database administrators (DBAs) set up customized databases to track items for each store, manufacturer, or any other commercial setup that needs to categorize and track data. For example, in a store that specializes in men's clothes, a DBA may set up a database with separate categories for each type of class, e.g., shirts, pants, underclothes, etc. The DBA may further subcategorize, or classify, these database categories. The “shirts” class may contain attributes such as neck size, color, sleeve length, etc., depending on the characteristics, or attributes, of the shirts that would typically be inventoried and sold by the store.
Clothing in the men's store is then inventoried and tracked with bar codes or item numbers for each item. Each such bar code or item number is related to a set class and set of attributes in the database. When a sales or inventory clerk in the store enters the bar code or item number into a computer, the code or number is electronically related to the class and its set of attributes in the database. This way, the store can automatically track the pricing of items, the quantity of each item in inventory, the quantity of items with certain attributes that need to be ordered to maintain an appropriate inventory, etc. For example, by entering a bar code or item number into a computer or electronic cash register, a sales clerk can retrieve pricing and sales information on each item. The store manager or ordering clerk can search the database and automatically retrieve information on the quantity of certain items in the store's inventory, thereby determining which items to order. Likewise, a buying planner can search the database to determine popular items and items with slow sales to analyze future buying and inventory needs for the store.
Data management for production lines has been similarly automated. DBAs configure software to create specific databases of classes and attributes for manufacturers. However, in both the store inventory and production line example, a great number of different configurations of classes and attributes per class are possible. For example, a department store may carry vast numbers of different items and many different vendors may supply each item, each vendor creating a like item with different attributes. A “Do-It-Yourself” store carries thousand of items for fixing a home. One such item, a screw, comes in hundreds of combinations of attributes such as bore width, thread size, length, material, etc. This results in a complex inventory database that must be able to accommodate many different screw configurations, as well as many different configurations of countless other items.
Likewise, production lines, for example in automobile production, use thousands of parts to create one type of item. The same production line can also be used to make many varieties of one type of item having different attributes and, therefore, requiring different parts. This, again, results in a complex inventory database that must be able to accommodate many different possible manufacturing configurations.
Adding to this complexity is the fact that each different store and each different manufacturer maintain differing types of inventories and have differing needs for their data management. Thus, database management software is typically personalized for each user as a fixed data model and requires a DBA to set up specific databases. Moreover, as stores, manufacturers, etc. acquire new material vendors or the vendors change their databases and classification systems, DBAs have to adjust the database management systems of the stores, manufacturers, etc. to properly track and maintain the changing classes and attributes of data.
Fixed data models in large databases are also used in electronic commerce. Besides requiring a DBA to set up and maintain each database on the internet, internet businesses face other burdens from using rigid table or database structures. Few internetworked businesses share the same ontologies. For example, an online vendor selling camera supplies may list different attributes for the same camera part or even list the same attributes under different labels than other online vendors. These differing ontologies complicate e-commerce by making it difficult for potential consumers or intermediate resellers to search and compare multiple e-vendors from the same web site.